Intro to Structured Analytic Techniques [Slides]
Structured Analytic Techniques are approaches that help us break down our thinking in a way that is deliberate, methodical, and transparent.
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Structured Analytic Techniques are approaches that help us break down our thinking in a way that is deliberate, methodical, and transparent.
As part of the Cyclotron Road program, Laminera Inc. worked towards development of atomic layer deposition process customizations and tool modifications to produce synthetic metal films at commercial scale and improve the film quality. Today's state-of-the-art in atomic layer deposited synthetic metal technology is only possible at a research-scale because current tools have practical limitations for scaling industrially and generating high conductivity films. Ultrathin, conformal synthetic metal films have many possible applications such as high surface area nanoporous electrodes for supercapacitors and water desalination devices; protective electrode coatings for electrochemical devices; seed layers for semiconductor device fabrication; or metallic component stabilizing coatings that enable enhanced storage density in next generation hard disk drives. The best synthetic metal conductive and plasmonic properties have been demonstrated using sputtering, which relies on high temperature sputtering with substrate dependent and directional growth limiting usage. This work explored refining process and tool modifications to create ultrathin, conformal, synthetic metal films that can be deposited with high quality over complex three-dimensional surfaces.
This report describes the results of initial welding trials for the purpose of implanting photoluminescent taggants in the form of metal-based oxides on the surface of a base material for future detection. The goal of this work was to explore the feasibility of using photoluminescent particles as surface tags for nuclear fuels and related materials (e.g., cladding). These tags could be used to store information about material provenance while being difficult to observe with the naked eye. This work is one of many related projects focused on nuclear fuel tagging, at the surface and bulk level, and is part of a multi-site Venture project. This tactic should not be viewed as a sole solution to tagging fuel, but rather a potential manufacturing strategy that may be implemented alongside other tagging strategies.
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Al HE generally has lower performance in Detonation Driving Zone but higher overall blast effects than non-aluminized HE. Objective is to develop benchtop system for dynamic x-ray diffraction (XRD) of aluminized HE.
Terrestrial ecosystems can show sustained responses to stress events that may last for years after the initial perturbation. This phenomenon is referred to as legacy or memory and it emerges from a set of ecosystem processes that are poorly captured by Earth System and Land Surface Models. Consequently, these models struggle to predict the impact that extreme climate events have on surface energy, carbon and hydrological exchange once a stressor such as drought has relaxed or in response to repeated exposure to stress. In this project, we set out to test how the addition of dynamic root profiles in land surface models impact the capacity for Earth System Models, such as the Department of Energy’s E3SM, to capture realistic legacy effects. Observations have shown that vegetation shifts their root profiles during stress events to forage for water and these altered root profiles may take years to relax back to the initial state. We hypothesized that the alteration of the belowground root structures may be a source of terrestrial ecosystem legacy that is missing from models. To test this idea we undertook three core activities. (1) We developed a global-scale analysis of the impact of dynamic roots on ecosystem legacy building from a recently developed dynamic root module. (2) We developed new root dynamics in the DOE’s Energy Land Model (ELM) that allowed not only root profiles to shift their profile but also simultaneously alter carbon allocation to fine root pools. We then tested the impact of these new dynamics with intensive sensitivity analysis at four long-term AmeriFlux sites. (3) We undertook detailed isotopic analysis of tree rings from these AmeriFlux sites to assess ecophysiological and ecohydrological legacy to stress events that can be used to benchmark the sensitivity experiments with ELM. From these core activities, we report the following key findings. Firstly, on a global scale, the addition of dynamic roots led to chronically water-stressed ecosystems recovering faster to climate stress while wetter ecosystem showed enhanced legacy. This is because across ecosystems, stress events were almost universally associated with water shortages that led to the development of deeper root profiles. These deeper root profiles proved beneficial for recovery from drought stress. While the root dynamics did not universally improve the modeled representation of legacy it showed complex transient dynamics that emerge from the addition of dynamic roots. Secondly, the sensitivity analysis illustrated long term shifts in rooting depths away from the prescribed default profile suggesting that initializing of root profiles could benefit from spin-up simulations that converge on locally optimized root profiles. In addition, by enabling dynamic allocation some of the sites predicted unrealistically low allocation to roots (and high allocation to leaves). While these changes did not dramatically alter modeled gross primary productivity, they illustrated that without more sophisticated root processes in models, there is little penalty to dramatically disinvest in roots. Thirdly, the isotopic analysis of tree rings showed highly distinct legacy responses across species and sites. For example, T. canadensis showed reduced transpiration the year after stress events illustrated by sustained elevated $\delta^{18}$O. In contrast, A saccharum displayed elevated $\delta^{13}$C associated with reduced stomatal conductance in response to the previous years’ stress event. These geochemical signatures of legacy were present despite tree growth returning to normal the year after the stress. The results show the importance of species-level dynamics in legacy that are absent in modeling that assumes common traits within plant functional types. In summary, the work here established new avenues to explore root dynamics in models while illustrating how additional root processes are needed before dynamic carbon allocation can be implemented. Lastly, this project provided mentorship to a postdoctoral fellow, training for an early career scientist and multiple undergraduate students recruited from a minority serving institution.
The goal of tagging nuclear fuels (or related materials, such as fuel cladding) is to provide a persistent marker, traceable to the fuel manufacturer, that can be detected after some length of time, likely after the fuel has been lost, rerouted, or partially destroyed. Ideally, the tag is complicated enough to encode data pertaining to the provenance (manufacturer, date or time, constituents, etc.) and robust enough to survive in-pile exposure and/or further destruction. The taggant should not change the behavior or performance of the fuel in any meaningful way. Photoluminescent tagging was an attractive solution to many of these challenges, as it is undetectable under normal lighting conditions, visible to the naked eye under specific lighting conditions, does not require a significant quantity of material to be photoluminescent, observable with simple commercial detection tools, and is versatile and robust in application. In FY23, the team focused on embedding photoluminescent taggants onto the replicate fuel cladding surface using welding. From a welding standpoint, photoluminescent taggants, in the form of oxides, are a viable option for tagging because the taggants have high melting points, and thus likely to survive welding temperatures (i.e., remain as a secondary phase particle and maintain luminescent properties). Additionally, these taggants have the potential to act as a metal matrix composite which may increase the strength-to-weight ratio, stiffness, and ductility of the base material, all beneficial to cladding and fuel performance.
The National Renewable Energy Laboratory (NREL) has published benchmark technical reports and presentations that disaggregate major product components for a variety of technologies, including land-based and offshore wind, photovoltaic, and battery energy storage systems. NREL has partnered with staff from the Pacific Northwest National Laboratory (PNNL) and members of the U.S. Department of Energy's Water Power Technology Office (WPTO) to create a non-exhaustive taxonomy of the major products and components of hydropower and pumped storage systems.
In this paper, we present a novel formulation of traditional sampling-based motion planners as database-oracle structures that can be solved via quantum search algorithms. We consider two complementary scenarios: for simpler sparse environments, we formulate the Quantum Full Path Search Algorithm (q-FPS), which creates a superposition of full random path solutions, manipulates probability amplitudes with Quantum Amplitude Amplification (QAA), and quantum measures a single obstacle free full path solution. For dense unstructured environments, we formulate the Quantum Rapidly Exploring Random Tree algorithm, q-RRT, that creates quantum superpositions of possible parent-child connections, manipulates probability amplitudes with QAA, and quantum measures a single reachable state, which is added to a tree. As performance depends on the number of oracle calls and the probability of measuring good quantum states, we quantify how these errors factor into the probabilistic completeness properties of the algorithm. We then numerically estimate the expected number of database solutions to provide an approximation of the optimal number of oracle calls in the algorithm. We compare the q-RRT algorithm with a classical implementation and verify quadratic run-time speedup in the largest connected component of a 2D dense random lattice. We conclude by evaluating a proposed approach to limit the expected number of database solutions and thus limit the optimal number of oracle calls to a given number.
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Explore the source record for details and available documents.
Explore the source record for details and available documents.
Explore the source record for details and available documents.